A simulation-based statistical method for planning modular construction manufacturing

Modular construction is a promising alternative to conventional construction; offering improved productivity, quality, and safety. To realize these benefits, sequencing the module fabrication process in a manner that ensures efficient allocation of labor resources is essential. However, the varying sizes and design specifications of modules lead to high variation in process times at workstations, ineffective utilization of resources, and imbalanced production lines. To address these challenges, this paper proposes a simulation-based statistical method to plan the sequencing of module fabrication and the allocation of workers at workstations for such that productivity and control are improved. The method consists of four processes: (i) data collection to obtain historical and near real-time data; (ii) identification of significant impact factors affecting process times at workstations along the production line; (iii) development of a predictive model for forecasting process times at workstations using statistical analysis and probability distribution function; and (iv) planning the sequencing of module fabrication in a manner that ensures efficient labor allocation (i.e., crew size). The developed method is validated using data captured from a light gauge steel wall panel production line operated by a modular fabricator in Edmonton, Canada. The industrial partner produces both interior and exterior light gauge steel wall panels on a production line consisting of multiple workstations. First, five significant impact factors for each workstation among the design factors that highly influence the process times were identified in order to develop cycle time formula as a predictive model. The simulation model developed and implemented in conjunction with cycle time formula (CTF) in this case study was deemed to be a reliable predictive model (i.e. 89.39% accuracy), which can be used to improve productivity. The method is shown to be capable of assisting in decision-making by enabling production managers to better understand the effects of proposed changes to the production line prior to implementation. In this way, production managers can plan effectively and thereby reducing non-productive idle time.